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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-data-analysis-real-world-applications/
课程评论：没有评论
课程名称：Python数据分析：现实世界应用 课程概述：欢迎参加《Python数据分析：现实世界应用》课程。我是您们的讲师Zaviir Berry，持有罗切斯特理工学院电气与计算机工程学位，专注于人工智能及其在实时脑电波数据分析中的应用，以分类人类运动功能。自2021年毕业以来，我在一家财富100强公司担任软件工程师。 在本课程中，您将： - 获得Python编程基础的扎实理解 - 学习如何处理数据集 - 可视化数据 - 执行机器学习和统计建模技术 我们将深入探讨模型开发的基本组成部分，包括： - 数据预处理 - 特征工程 - 模型训练 - 评估 本课程结束后，参与者将掌握有效预测保险索赔金额和金融市场趋势所需的技能，能够利用患者的特征（如年龄、性别、体重指数（BMI）和血压）来准确预测保险索赔金额。此外，参与者将在高精度下预测S&P 500的下一日收盘价。课程中还包括全面的数据预处理模块，使参与者能够有效地为各种机器学习技术准备数据，如线性回归和逻辑回归。通过应用准确率、精确度和召回率等多种评估指标，参与者将能够解读模型结果，从而基于预测做出明智决策。
Welcome to Python Data Analysis: Real World Applications. I am Zaviir Berry, your instructor for this comprehensive course. I hold a degree in Electrical and Computer Engineering from Rochester Institute of Technology where I specialized in artificial intelligence and its applications in analyzing live brain wave data to classify human motor functions. Since graduating in 2021, I have been working as a Software Engineer at a Fortune 100 company.Throughout this course, you will: gain a solid understanding of the basics of Python programminglearn how to work with datasetsvisualize dataperform machine learning and statistical modeling techniquesWe will delve into the essential components of model development, including: data preprocessingfeature engineeringmodel trainingevaluationUpon completion of this course, participants will have acquired the skills necessary to effectively forecast insurance claim amounts and predict financial market trends using advanced machine learning techniques. They will be able to utilize patient characteristics, such as age, gender, Body Mass Index (BMI), and blood pressure, to make accurate predictions of insurance claim amounts. Additionally, they will be able to predict the closing price of the S & P 500 for the next day with a high degree of accuracy. The course also includes a comprehensive data preprocessing component, which enables participants to effectively prepare data for use in various machine learning techniques, including Linear and Logistic Regression. Furthermore, participants will be able to interpret the results of their models through the application of various evaluation metrics, such as accuracy, precision, and recall, which will allow them to make informed decisions based on their predictions.
